LoneStriker
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Browse files- .gitattributes +1 -0
- README.md +57 -0
- TheBeagle.png +3 -0
- config.json +25 -0
- model.safetensors.index.json +1 -0
- output.safetensors +3 -0
- special_tokens_map.json +35 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +48 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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TheBeagle.png filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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license: cc-by-nc-nd-4.0
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tags:
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- generated_from_trainer
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model-index:
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- name: UNA-TheBeagle-7b-v1
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results: []
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datasets:
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- jondurbin/bagel-v0.3
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library_name: transformers
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---
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-- In the Love Memory of my "LoLa" --
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# UNA-TheBeagle-7b-v1
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TheBeagle, a model of 7B parameters trained on The Bagel dataset. DPO & UNA applied over a set of curated DPO Pairs.
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- Scored #1 on the HF Leaderboard, dramatic scores!!! 73 ARC, and very well balanced!
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The dataset was generated using the original bagel code, including the decontamination step.
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As base model, we used the latest Intel's neural-chat model.
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It performs very good in many tasks, but its always better that you play with it by yourself.
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![TheBeagle](https://huggingface.co/fblgit/UNA-TheBeagle-7b-v1/resolve/main/TheBeagle.png)
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## Evaluations
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Ran with VLLM so expect them to dont be exactly as the one's shown in the board, but not too far :)
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```
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vllm (pretrained=fblgit/UNA-TheBeagle-7b-v1,dtype=auto,tensor_parallel_size=1,gpu_memory_utilization=0.8,data_parallel_size=8,trust_remote_code=True), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 32
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| Tasks |Version| Filter |n-shot| Metric |Value | |Stderr|
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|--------------|-------|----------|-----:|-----------|-----:|---|-----:|
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|arc_challenge |Yaml |none | 25|acc |0.7090|± |0.0133|
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| | |none | 25|acc_norm |0.7329|± |0.0129|
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|gsm8k |Yaml |get-answer| 5|exact_match|0.7210|± |0.0124|
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|hellaswag |Yaml |none | 10|acc |0.7202|± |0.0045|
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| | |none | 10|acc_norm |0.8792|± |0.0033|
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|truthfulqa_mc2|Yaml |none | 0|acc |0.7062|± |0.0151|
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|winogrande |Yaml |none | 5|acc |0.8366|± |0.0104|
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```
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## UNA Details
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For this release, we only applied UNA thru the perceptrons. It was done at a 3.5e-7 speed, and the training loop code is also the original one of the bagel and transformers-4.35.2-UNA
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## Prompt
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Im not entirely sure of it, as we used the vanilla version of the bagel training code. But a good model should be able to generalize with different prompt formats, so feel free to give it a shot.
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## Citations
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Remember if you use UNA's models, cite it in your model card.
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## Limitations
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Not for commercial use, and only for academic & research purposes.
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TheBeagle.png
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Git LFS Details
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config.json
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{
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"architectures": [
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"MistralForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 32768,
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"model_type": "mistral",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"rms_norm_eps": 1e-05,
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"rope_theta": 10000.0,
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"sliding_window": 4096,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.36.2",
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"use_cache": false,
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"vocab_size": 32000
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}
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model.safetensors.index.json
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